Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide.
• Currently, no predictive molecular biomarkers exist to accurately determine CRC disease stage.
• Molecular patterns offer potential for personalized treatment strategies.

Purpose of the Study:

• To identify and characterize molecular subtypes of colorectal cancer (CRC).
• To analyze gene expression patterns and protein-protein interaction networks within each subtype.
• To explore potential therapeutic targets based on subtype-specific functional modules.

Main Methods:

• Utilized microarray data from CRC and adjacent normal tissues.
• Categorized samples into four consensus molecular subtypes (CMS) based on gene expression.
• Performed weighted gene-based protein-protein interaction network analysis for each subtype.
• Identified significant functional modules (NUSAP1, CD44, COL4A1) across all subtypes.

Main Results:

• Identified four distinct consensus molecular subtypes (CMS) of colorectal cancer.
• NUSAP1, CD44, and COL4A1 modules were significant and present across all subtypes.
• The CMS4-mesenchymal subtype, associated with poor prognosis, showed enrichment in immune, stromal, ECM dysregulation, and chemokine processes.
• Hub gene analysis within protein-protein interaction networks suggested potential therapeutic targets.

Conclusions:

• Functional modules derived from gene expression signatures are robust for subtype classification.
• Subtype-specific functional module analysis provides insights into CRC biology.
• This approach can guide the discovery of novel treatment targets and inform clinical management for colorectal cancer.